The role of feedback in morphological computation with compliant bodies

Biological Cybernetics

Volumn 106, Issue 10, 2012, Pages 595-613

  Hauser, Helmut ^a   Ijspeert, Auke J ^c   Füchslin, Rudolf M ^a,b   Pfeifer, Rolf ^a   Maass, Wolfgang ^d  

^a UNIVERSITY OF ZURICH   (Switzerland)

^b Zurich University of Applied Science   (Switzerland)

^c EPFL   (Switzerland)

^d GRAZ UNIVERSITY OF TECHNOLOGY   (Austria)

Author keywords

Compliant robots; Limit cycles; Morphological computation; Nonlinear system

Indexed keywords

AUTONOMOUS GENERATION; BIOLOGICAL ORGANISMS; COMPUTING SYSTEM; CONTROL PRINCIPLE; EXTERNAL SIGNALS; FADING MEMORY; LIMIT CYCLE; LINEAR FEEDBACK; MASS SPRING SYSTEMS; MORPHOLOGICAL COMPUTATION; NONLINEAR FEATURES; PERIODIC PATTERN; ROBOTIC SYSTEMS;

NONLINEAR SYSTEMS; ROBOTICS; ROBOTS;

COMPUTER SIMULATION;

ARTICLE; COMPUTER SIMULATION; FEEDBACK SYSTEM;

COMPUTER SIMULATION; FEEDBACK;

EID: 84870891820     PISSN: 03401200     EISSN: 14320770     Source Type: Journal    
DOI: 10.1007/s00422-012-0516-4     Document Type: Article

References (41)

1. Bartlett PL, MaassW (2003) Vapnik-Chervonenkis dimension of neural nets. In: Arbib MA (ed) The handbook of brain theory and neural networks, 2nd edn. MIT Press, Cambridge, pp 1188-1192
2. Bishop CM (1994) Training with noise is equivalent to tikhonov regularization. Neural Comput 7:108-116
3. Collins S, Ruina A, Tedrake R, WisseM (2005) Efficient bipedal robots based on passive-dynamic walkers. Science 307:1082-1085
4. Franceschini N, Pichon JM, Blanes C, Brady JM (1992) From insect vision to robot vision. Phil Trans R Soc Lond B 337(1281):283-294
5. Hauser H, IjspeertA, Füchslin RM, Pfeifer R, MaassW(2012) Towards a theoretical foundation formorphological computation with compliant bodies. Biol Cybern 105(5):355-370. ISSN 0340-1200
6. Hoerzer G, Legenstein R, Maass W (2011) Eliminating the teacher in reservoir computing. In: Pfeifer R, Sumioka H, Füchslin RM, Hauser H, Nakajima K, Miyashita S (eds) Proceedings of the 2nd international conference on morphological computation, Venice
7. Hoerzer GM, Legenstein R, Maass W (2012) Emergence of complex computational structures from chaotic neural networks through reward-modulated hebbian learning (in preparation)
8. Hoffmann M, Marques H, Hernandez Arieta A, Sumioka H, Lungarella M, Pfeifer R (2010) Body schema in robotics: a review. IEEE Trans Auton Mental Develop 2(4):304-324
9. Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Networks 2:359-366
10. Iida F, Pfeifer R (2006) Sensing through body dynamics. Robot Auton Syst 54(8):631-640
11. Ijspeert A, Crespi A, Ryczko D, Cabelguen J-M (2007) From swimming to walking with a salamander robot driven by a spinal cord model. Science 315(5817):1416-1420
12. Isidori A (2001) Nonlinear control systems, 3rd edn. Springer, London
13. Khalil HK (2002) Nonlinear systems, 3rd edn. Prentice Hall, Englewood Cliffs
14. Legenstein R, Chase SM, Schwartz AB, Maass W (2010) A rewardmodulated Hebbian learning rule can explain experimentally observed network reorganization in a brain control task. J Neurosci 30(25):8400-8410
15. Lukoševicius M, Jaeger H (2009) Reservoir computing approaches to recurrent neural network training. Comput Sci Rev 3(3):127-149
16. Maass W, Natschlaeger T, Markram H (2002) Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural Comput 14(11):2531-2560
17. Maass W, Joshi P, Sontag ED (2007) Computational aspects of feedback in neural circuits. PLoS Comput Biol 3(1):e165
18. MartiniHF, Nath JL, BartholomewEF (2011) Fundamentals of anatomy & physiology, 9th edn. Benjamin-Cummings Publishing Company, Menlo Park
19. McGeer T (1990) Passive dynamic walking. Int J Rob Res 9(2):62-82
20. Miyashita S, Göldi M, Pfeifer R (2011) How reverse reactions influence the yield rate of stochastic self-assembly. Int J Robot Res 30:627-641
21. PalmWJIII (1999) Modeling, analysis, and control of dynamic systems, 2nd edn. Wiley, New York
22. Paul C (2006) Morphological computation: A basis for the analysis of morphology and control requirements. Robot Auton Syst 54(8):619-630
23. Paul C, Valero-Cuevas FJ, Lipson H (2006) Design and control of tensegrity robots for locomotion. IEEE Trans Robot 22(5):944-957
24. Pfeifer R, Bongard JC (2007) How the body shapes the way we think. The MIT Press, Cambridge
25. Pfeifer R, Lungarella M, Iida F (2007) Self-organization, embodiment, and biologically inspired robotics. Science 318:1088-1093
26. Rieffel J, Trimmer B, Lipson H (2008) Mechanism asmind: what tensegrities and caterpillars can teach us about soft robotics. In: Bullock S, Noble J, Watson R, Bedau MA (eds) Artificial life XI: proceedings of the eleventh international conference on the simulation and synthesis of living systems, pp 506-512. MIT Press, Cambridge
27. Righetti L, Ijspeert AJ (2008) Pattern generators with sensory feedback for the control of quadruped locomotion. In: IEEE international conference on robotics and automation, pp 819-824. ICRA 2008, Pasadena, 19-23 May 2008
28. Schrauwen B, Verstraeten D, Van Campenhout J (2007) An overview of reservoir computing: theory, applications and implementations. In: Proceedings of the 15th European symposium on artificial neural networks pp 471-482. ESANN, Bruges
29. Shaw RS, Packard N, Schröter M, Swinney HL (2007) Geometryinduced asymmetric diffusion. PNAS 104(23):9580-9584
30. Shim Y, Husbands P (2007) Feathered flyer: integrating morphological computation and sensory reflexes into a physically simulated flapping-wing robot for robust flight manoeuvre. In: Almeida e Costa F et al. (eds) ECAL, pp 756-765. Springer, Berlin
31. Slotine J-JE, Li W (1991) Applied nonlinear control, 1st edn. Prentice Hall, New York
32. Sontag ED (1998) Mathematical control theory: deterministic finite dimensional systems, 2nd edn. Springer, New York
33. TagaG (1998) Amodel of the neuro-musculo-skeletal system for anticipatory adjustment of human locomotion during obstacle avoidance. Biol Cybern 78(1):9-17
34. Tedrake R, Zhang TW, Seung HS (2005) Learning to walk in 20 minutes. In: Proceedings of the fourteenth yale workshop on adaptive and learning systems, Yale University, New Haven
35. Vapnik VN (1998) Statistical learning theory. Wiley, New York
36. Whitesides GM, Grzybowski B (2002) Self-assembly at all scales. Science 295(5564):2418-2441
37. Wisse M, van der Linde RQ (2007) Delft pneumatic bipeds, vol 34. Springer Tracts in Advanced Robotics. Springer, Berlin
38. Wisse M, Van Frankenhuyzen J (2003) Design and construction of MIKE: a 2D autonomous biped based on passive dynamicwalking. In: Proceedings of international symposium of adaptivemotion and animals and machines (AMAM03), Kyoto
39. Wood RJ (2007) Design, fabrication, and analysis of a 3DOF, 3 cm flapping-wing MAV, pp 1576-1581. IROS, San Diego
40. Wyffels F, Schrauwen B (2009) Design of a central pattern generator using reservoir computing for learning human motion. In: ATEQUAL 2009: 2009 ECSIS symposium on advanced technologies for enhanced quality of life (LABRS and ARTIPED 2009): proceedings, pp 118-122. IEEE Computer Society, Los Alamitos
41. Ziegler M, Iida F, Pfeifer R (2006) "Cheap" underwater locomotion: roles of morphological properties and behavioural diversity. In: International Conference on Climbing andWalking Robots, CLAWAR, Karlsruhe